Students who are Black or Hispanic are now reported to be less likely to be identified as having disabilities than similarly situated students who are White. Although repeatedly replicated, this finding is often characterized as in error. I use a new statistical technique, the E-value, to quantify the likelihood that unmeasured confounding explains observed associations between race or ethnicity and disability identification. Results based on calculations across three population-based studies using extensive statistical controls suggest that unmeasured confounding is an unlikely explanation for the observed associations. Unmeasured confounding that would result in levels of overidentification consistent with federal law and regulation is especially unlikely.

据报道，黑人或西班牙裔学生比白人学生处于同等地位的学生更容易被识别为有残疾。尽管反复重复，但这一发现通常被认为是错误的。我使用一种新的统计技术E值来量化未测混杂因素解释观察到的种族或族裔与残疾识别之间的关联的可能性。根据对三项基于人群的研究的计算结果，使用广泛的统计控制方法得出的结果表明，无法测量的混杂因素对观察到的关联性不太可能解释。尤其是不太可能发生无法衡量的混淆，从而导致过度识别的程度与联邦法律和法规相一致。